Data Deduplication Approaches: Concepts, Strategies, and Challenges

In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability. Includes data deduplication methods for a wide variety of applicationsIncludes concepts and implementation strategies that will help the reader to use the suggested methodsProvides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applicationsFocuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approachesIncludes case studies INDICE: 1. Introduction to Data Deduplication Approaches2. Data Deduplication Concepts3. Concepts, Strategies, and Challenges of Data Deduplication4. Existing Mechanisms for Data Deduplication5. Classification Criteria for Data Deduplication Methods6. File Chunking Approaches7. Study of Data Deduplication for File Chunking Approaches8. Essentials of Data Deduplication Using Open Source Toolkit9. Efficient Data Deduplication Scheme for Scale-Out Distributed Storage10. Identification of Duplicate Bug Reports in Software Bug Repositories: A Systematic Review, Challenges and Future Scope11. A Survey and Critical Analysis on Energy Generation from Datacenter12. Review of MODIS EVI and NDVI Data for Data Mining Applications13. Performance Modelling for Secure Migration Processes of Legacy Systems to the Cloud Computing14. DedupCloud: An Optimized Efficient VM Deduplication Algorithm in Cloud Computing Environment15. Data Deduplication for Cloud Storage16. The Data Deduplication Using AWS Cloud Storage17. Game Theoretic Analysis of Encrypted Cloud Data Deduplication18. Data Deduplication Applications in Cognitive Science and Computer Vision Research

• ISBN: 978-0-12-823395-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 332
• Fecha Publicación: 01/02/2021
• Nº Volúmenes: 1
• Idioma: Inglés